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A Review and Analysis of GAN-Based Super-Resolution Approaches for INSAT 3D/3DR Satellite Imagery using Artificial Intelligence

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Title A Review and Analysis of GAN-Based Super-Resolution Approaches for INSAT 3D/3DR Satellite Imagery using Artificial Intelligence
 
Creator Rajamohana, S P
Thamaraiselvi, S
R, Bibraj
Mitha, Samir
 
Subject Deep learning
Generative adversarial network
Meteorology
Remote sensing
Weather monitoring
 
Description 627-638
The Indian National Satellite System (INSAT)-3D/3DR is a geostationary satellite that is used for meteorological
applications in the Indian region. Geostationary satellites have significant spatial coverage and good temporal resolution that
help to monitor the evolution and propagation of meteorological systems. Meteorologists use satellite images to observe the
locations of severe weather and understand the physical processes involved in the system. Image Super-Resolution (SR)
aims to convert low-resolution images into high-resolution images while maintaining image quality. The SR techniques will
improve the visualization of convective systems and tropical cyclones, facilitating accurate location-based warnings. This
paper presents a comparative comparison of computer models for converting Low-Resolution (LR)(INSAT)-3D/3DR
images into super-resolution images. This study also discusses and investigates the various Generative Adversarial Network
(GAN)-based models, including the Super Resolution Generative Adversarial Network (SRGAN), Enhanced Super
Resolution Generative Adversarial Network (ESRGAN), and Real Enhanced Super Resolution Generative Adversarial
Network (Real-ESRGAN). The findings are compared to established approaches such as Bicubic Interpolation and Super-
Resolution Convolution Neural Network (SRCNN). This study demonstrates that Real-ESRGAN performs better on weather
satellite images than other cutting-edge approaches.
 
Date 2024-06-07T09:45:32Z
2024-06-07T09:45:32Z
2024-06
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/64047
https://doi.org/10.56042/jsir.v83i6.7320
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.83(6) [June 2024]